Identifying IoT devices is crucial for network monitoring, security
enforcement, and inventory tracking. However, most existing identification
methods rely on deep packet inspection, which raises privacy concerns and adds
computational complexity. More importantly, existing works overlook the impact
of wireless channel dynamics on the accuracy of layer-2 features, thereby
limiting their effectiveness in real-world scenarios. In this work, we define
and use the latency of specific probe-response packet exchanges, referred to as
"device latency," as the main feature for device identification. Additionally,
we reveal the critical impact of wireless channel dynamics on the accuracy of
device identification based on device latency. Specifically, this work
introduces "accumulation score" as a novel approach to capturing fine-grained
channel dynamics and their impact on device latency when training machine
learning models. We implement the proposed methods and measure the accuracy and
overhead of device identification in real-world scenarios. The results confirm
that by incorporating the accumulation score for balanced data collection and
training machine learning algorithms, we achieve an F1 score of over 97% for
device identification, even amidst wireless channel dynamics, a significant
improvement over the 75% F1 score achieved by disregarding the impact of
channel dynamics on data collection and device latency.

通过累积分数为数据收集和机器学习算法训练引入的方法，在考虑到无线信道的动态影响时，成功实现了超过 97% 的设备识别的 F1 分数，相较于忽略信道动态对数据收集和设备延迟的影响时仅实现 75% 的 F1 分数，实现了显著改进。